The modern consumer is increasingly demanding and immediate. Therefore, a conversational bot that doesn’t understand the user ends up bringing more disadvantages than advantages, leading to extra costs for companies and customer dissatisfaction.

For us, who are used to getting search results on Google in a matter of seconds and are also adapting to customer service bots, building a conversational bot might seem like a simple task. But believe me, it’s not.

In this article, you will learn what NLP is, how this resource works, and examples of use that can be applied to your business.

Enjoy the reading!

The concept of NLP

NLP (Natural Language Processing) is a technology that enables devices to converse via text or voice with a person.

This is because programming languages (for example: Java, Python, Ruby, and others) are very different from Portuguese, English, and other languages through which we, humans, normally communicate.

NLP, therefore, serves as a translator so that devices and technological solutions can understand what we write or say, even if it’s not in the programming language.

And in addition to understanding, NLP allows technologies to respond to human interactions and queries, as in the case of customer service bots (chatbots and voicebots) used by companies on websites and communication platforms.

How it works

The word “bank,” for example, can mean a bench or a financial institution. We, humans, understand the difference according to the context. The technology, to understand such cases, uses NLP.

NLP enables technologies to consider aspects like the context of the conversation, understand syntactic and semantic meanings, and also interpret texts, analyze sentiments, and much more.

To handle text interpretation and complex dialogues, NLP can learn from human interactions and evolve its conversational capability.

Of course, behind NLP, there is an entire multidisciplinary team with developers, programmers, data scientists, content curators, copywriters, computational linguists, psychologists, anthropologists, and AI, UX specialists, etc.

A whole structure is needed to create a knowledge base, where vocabulary, tone of voice, and the entire conversational flow, along with all possible responses that the technology can provide to the customer, are defined.

All of this ensures that computers and other technological solutions understand, respond, and learn from human interactions. This leads us to understand that NLP is just one of the elements of AI and Machine Learning, as part of a package aimed at improving the user experience.

Deep Dive

With NLP, conversational bots are created with a high level of interaction complexity with humans thanks to highly specialized development tools.

Through NLP, robots are capable of performing complex conversational flows, leading a natural conversation with the user, simulating a human conversation to resolve a procedure or demand.

It is now possible to deliver a conversational bot capable of solving any type of contact reason, from a simple static response to scheduling a technical visit with a technical support sales negotiation.

To execute these procedures, customer service bots use systems integrated with the client’s platforms to query data or perform an action to resolve the user’s demand.

Thus, it’s possible to create a fluid and natural interaction with the user through text or voice messages. But we remind you that the goal is never to try to deceive the user, but to make the conversation so natural that the user forgets they’re talking to a bot.

Examples of Use

Check out where NLP is commonly embedded!

Search Engines

In search engines, NLP interprets what users are looking for and also analyzes what the content says, presenting the best and most relevant possible results.

Additionally, in search engines, NLP also makes suggestions about the user’s query. This happens when you start typing a question in Google, for example, and the search is automatically completed.

Voice Assistants

Alexa and Siri are examples of virtual assistants activated by voice, which recognize user commands in seconds, and from there, are capable of playing your favorite music, turning on your coffee maker, etc.

Customer Service Bots

Customer service bots, such as chatbots and voicebots, are already quite common as first contact in call centers, aiming to filter repetitive and simple problems to free up time for attendants to focus on high-value issues.

This is undoubtedly the primary example of NLP usage, and also the one that brings the most benefits to companies. This is because bots can handle hundreds or even thousands of simultaneous interactions. Thus, companies reduce personnel costs and optimize their processes, increasing employee productivity.

That’s it! We’ve reached the end of this article on NLP, and we hope you’ve understood that it’s one of the most important elements of AI, especially when it comes to customer service bots for businesses.

It was a pleasure to have you with us. See you soon!

Also read: Customer Experience (CX): Learn everything about it.